To use the Deepspeech model from Mozilla, and use it extensively for identifying the software engineering jargon to assist developers in sorting issues and debug their code.
- Deepspeech Documentation
- Deepspeech Paper
- KenLM Library
- Scripts obtained from Object-Oriented-Design from the University of Alberta on Coursera, and
- Server-Side Development from The Hong Kong University of Science and Technology.
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Back-End Web development text scripts.
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Object Oriented Design text scripts.
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Notebooks for appropriate formatting and work up to make scripts suitable for training a scorer have been named according to their function.
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Scorers were built here.
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Follow the External Scorer Scripts documentation for building and training your own scorers according to the dataset.
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STEPS TO BUILD A SCORER
gzip OO.txt
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Build the KenLM Library.
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Use the following command to build the lm.binary and vocab.txt, as done in createVocab.sh.
python3 generate_lm.py --input_txt oo.txt.gz --output_dir . \ --top_k 500000 --kenlm_bins ../../kenlm/build/bin/ \ --arpa_order 5 --max_arpa_memory "85%" --arpa_prune "0|0|1" \ --binary_a_bits 255 --binary_q_bits 8 --binary_type trie
- To build the scorer, run the following command, as done in build.sh
- Fine tune values of parameters
default_alpha
anddefault_beta
for obtaining signals appropriately.
./generate_scorer_package --alphabet ../alphabet.txt --lm lm.binary --vocab vocab-500000.txt \ --package kenlm.scorer --default_alpha 0.93 --default_beta 1.18 --force_bytes_output_mode True
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Audio file obtained from Object-oriented Programming in 7 minutes | Mosh.
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Used convert.ipynb for extracting the audio from the video and converting it into .wav format.
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To obtain the result script and store it into result.txt run the following command in terminal.
deepspeech --model deepspeech-0.9.3-models.pbmm --scorer kenlm.scorer --audio OO.wav > result.txt